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Richard I. Cullather and Michael G. Bosilovich

represented in the assimilating weather forecast model. In particular, surface albedo characteristics over polar oceans and high-latitude cloud properties are both associated with important but complex energy feedback mechanisms that have historically been poorly simulated ( Randall et al. 1998 ). An initial evaluation of the high-latitude energy budget in a reanalysis record is therefore a constructive activity. Some questions of interest pertaining to this study are as follows. What are the spatial and

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Richard I. Cullather and Michael G. Bosilovich

–sea ice, land surface, and limited-area atmospheric models (e.g., Walsh et al. 2002 ; Rinke et al. 2006 ). Notwithstanding these wide-ranging and constructive applications, reanalyses contain some degree of uncertainty because of the limitations in the observing systems, inconsistencies between differing observations, and incomplete knowledge of the physical processes that are represented in the background weather forecast model (e.g., Thorne 2008 ; Grant et al. 2008 ; Bitz and Fu 2008 ; Hines

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ChuanLi Jiang, Sarah T. Gille, Janet Sprintall, Kei Yoshimura, and Masao Kanamitsu

resolving smaller scale features than the older NWP products. Recently the European Centre for Medium-Range Weather Forecasts released more than two years (May 2008–present) of data from their high-resolution operational product in support of the Year of Coordinated Observing Modeling and Forcasting Tropical Convection (YOTC) ( Waliser and Moncrieff 2008 ), hereafter referred to as ECMWF-YOTC. Dynamical downscaling ( Kanamitsu and Kanamaru 2007 ) offers another strategy for obtaining small-scale fluxes

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Ivana Cerovečki, Lynne D. Talley, and Matthew R. Mazloff

the problem of estimating fluxes ( Gulev 2003 ). The final report ( Taylor 2000 , hereafter WGASF ) concluded that, at the time of the report, “all existing flux estimates have deficiencies.” Since the time of the WGASF report, a number of new contemporary global reanalyses have been developed as follows: the Japanese 25-yr Reanalysis Project (JRA-25), 1979–present; the European Centre for Medium-Range Weather Forecasts’ (ECMWF) “interim” Reanalysis (ERA-Interim), 1989–present; the National

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Xiaolei Niu and Rachel T. Pinker

radiative fluxes. They have been evaluated against ARM-NSA observations of radiative fluxes and cloud products. The intercomparison of four reanalysis products [the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), the NCEP–NCAR North American Regional Reanalysis (NARR), and the Japan Meteorological Agency and Central Research Institute of

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Xiangzhou Song and Lisan Yu

-E), Advanced Very High Resolution Radiometer (AVHRR), Special Sensor Microwave Imager (SSM/I), and Quick Scatterometer (QuikSCAT)] and the surface meteorology from reanalysis/forecast models [e.g., National Centers for Environment Prediction (NCEP), the 40-yr European Centre for Medium-range Weather Forecasting (ECMWF) Re-Analysis (ERA-40), and ERA-Interim ( Yu and Weller 2007 ; Yu et al. 2008 )]. The objective analysis is used to obtain optimal estimates of flux-related surface meteorology, and the

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Lei Shi, Ge Peng, and John J. Bates

nine pairs of satellites from SNO observations. b. Neural network retrieval A neural network approach is used to develop temperature and humidity retrieval algorithms. The training dataset is constructed by using a radiative transfer model, the Radiative Transfer for Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder version 9 (RTTOV-9), to simulate a diverse sample of reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) system ( Chevallier

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Sohey Nihashi, Kay I. Ohshima, and Noriaki Kimura

appropriate in their heat flux calculation. For example, sea ice was treated as weekly ice cover, without considering its concentration, in the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) reanalysis dataset ( Kalnay et al. 1996 ) and in the 40-yr European Centre for the Medium-Range Weather Forecasts Re-Analysis (ERA-40) dataset ( Uppala et al. 2005 ). Ice concentration was taken into account in the Ocean Model Intercomparison Project (OMIP

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